Dynamic monitoring and securing of factory processes, equipment and automated systems

ABSTRACT

A system including a deep learning processor receives one or more control signals from one or more of a factory&#39;s process, equipment and control (P/E/C) systems during a manufacturing process. The processor generates expected response data and expected behavioral pattern data for the control signals. The processor receives production response data from the one or more of the factory&#39;s P/E/C systems and generates production behavioral pattern data for the production response data. The process compares at least one of: the production response data to the expected response data, and the production behavioral pattern data to the expected behavioral pattern data to detect anomalous activity. As a result of detecting anomalous activity, the processor performs one or more operations to provide notice or cause one or more of the factory&#39;s P/E/C systems to address the anomalous activity.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/904,984, filed Jun. 18, 2020, which is a continuation-in-part of U.S.patent application Ser. No. 16/781,193, filed Feb. 4, 2020, which claimsthe benefit of U.S. Provisional Application No. 62/950,588, filed Dec.19, 2019. U.S. patent application Ser. No. 16/904,984 also claims thebenefit of U.S. Provisional Application No. 62/983,487, filed Feb. 28,2020, U.S. Provisional Application No. 62/912,291, filed Oct. 8, 2019,U.S. Provisional Application No. 62/931,453, filed Nov. 6, 2019, U.S.Provisional Application No. 62/932,063, filed Nov. 7, 2019, and U.S.Provisional Application No. 62/938,158, filed Nov. 20, 2019. Thisapplication is related to U.S. patent application Ser. No. 16/663,245,filed Oct. 24, 2019. All of the foregoing are incorporated by referencein their entireties.

BACKGROUND 1. Technical Field

The present disclosure generally relates to systems, apparatuses andmethods for dynamically monitoring and securing factory processes,equipment and control systems against attacks that can interfere with afactory's operation and control.

2. Introduction

Malware attacks against factories are proliferating and becoming verysophisticated. Further, these malware attacks are often capable ofpenetrating isolated and closed computer networks, as well as machinesconnected to external networks (e.g., 4G and 5G networks). Many of theseattacks often target a factory's processes, equipment and control(“P/E/C”) systems (sometimes referred to herein as, the “operation andcontrol of factories”). Malware, as used herein, refers to any hardwareor software that causes damage, disruption, or unauthorized manipulationor access, for example, to a computer, server, controller, computernetwork, computer-controlled equipment, data, or the quality or yield ofa final output. Malware can include computer viruses, worms, Trojanhorses, spyware, backdoors, or generally any program or file that can beharmful to a computer system. Although in most cases malware isdeliberately designed to inflict damage, disruption or provideunauthorized access or manipulation (collectively, “interference”),interference can also occur from nonintentional introductions ofsoftware and/or hardware. Malware can take many forms including, but notlimited to, computer viruses, worms, Trojan horses, spyware, backdoors,faulty components. Malware can be designed to cause subtle changes tothe operation and control of factories and are often able to evadeconventional information technology (IT) security solutions orconventional process control systems. While the changes to the operationand control of factories may be subtle, the impact of the malwareattacks on the factories' output and equipment can be severe andcatastrophic. For instance, malware attacks can be directed atprogrammable logic controllers or other controllers, which control afactory's processes and equipment, to alter the controllers' programmingin a damaging way (e.g., by instructing equipment to operate faster orslower than prescribed, by introducing rapid or frequent changes tocontrol parameters, by increasing or decreasing the control parametersat greater increments than prescribed). Additionally, these attacks canprovide false feedback to the controllers that the equipment isoperating at normal levels. As a result, the controllers can receivefeedback that everything is operating normally, which can cause ITsecurity solutions or conventional process control systems to not beactivated. Thus, the equipment can continue to operate at abnormallevels until the equipment or the output becomes irreversibly damagedand the yield noticeably diminished. Malware can enable a range ofnegative outcomes from the degradation of efficiency and yield ofongoing production to the catastrophic failure of key systems and longterm stoppage of production.

Accordingly, it is desirable to provide a new mechanism for dynamicallysecuring factory processes, equipment and control systems by dynamicallydetecting anomalous activity, however subtle, before serious damage to afactory's P/E/C systems and final output occurs.

SUMMARY

In one example, a computer-implemented method includes receiving, by adeep learning processor, one or more control signals from one or more ofa factory's process, equipment and control (P/E/C) systems during amanufacturing process; generating, by a deep learning processor,expected response data and expected behavioral pattern data for thecontrol signals; receiving, by the deep learning processor, productionresponse data, from the one or more of the factory's P/E/C systems;generating, by the deep learning processor, production behavioralpattern data for the production response data; comparing at least oneof: (i) the production response data to the expected response data, and(ii) the production behavioral pattern data to the expected behavioralpattern data to detect anomalous activity; and as a result of detectingthe anomalous activity, performing one or more operations to providenotice or cause one or more of the factory's P/E/C systems to addressthe anomalous activity in the manufacturing process.

In some examples, the one or more operations include: determiningwhether the anomalous activity is a malware attack; and as a result of adetermination that the anomalous activity is the malware attack,initiating an alert protocol to provide notice or cause one or more ofthe factory's P/E/C systems to address the anomalous activity in themanufacturing process.

In some examples, the alert protocol is a digital activation ofindividual relays communicated to one or more devices associated withthe factory's P/E/C systems to provide the notice or cause one or moreof the factory's P/E/C systems to address the anomalous activity in themanufacturing process.

In some examples, the one or more operations include shutting down themanufacturing process.

In some examples, the production response data is derived by adjustingsetpoints associated with one or more process stations associated withthe one or more of the factory's P/E/C systems.

In some examples, the anomalous activity is detected as a result of theproduction response data and the expected response data indicating adeviation.

In some examples, the anomalous activity is detected as a result of theproduction behavioral pattern data and the expected behavioral patterndata indicating a deviation.

In some examples, the one or more operations include transmitting anotification to an operator of the manufacturing process to review theanomalous activity.

In some examples, the computer-implemented method further includesdetermining, based on a comparison of the production response data tothe expected response data, a confidence level associated with anidentification of the anomalous activity; and identifying, based on theconfidence level, the one or more operations to be performed to providenotice or cause one or more of the factory's P/E/C systems to addressthe anomalous activity in the manufacturing process.

In some examples, the computer-implemented method further includesdetermining, based on the comparison of production behavioral patterndata to the expected behavioral pattern data, a confidence levelassociated with an identification of the anomalous activity; andidentifying, based on the confidence level, the one or more operationsto be performed to provide notice or cause one or more of the factory'sP/E/C systems to address the anomalous activity in the manufacturingprocess.

In one example, a system includes one or more processors, and memorystoring thereon instructions that, as a result of being executed by theone or more processors, cause the system to: receive one or more controlsignals from one or more of a factory's process, equipment and control(P/E/C) systems during a manufacturing process; generate expectedresponse data and expected behavioral pattern data for the controlsignals; receive production response data from the one or more of thefactory's P/E/C systems; generate production behavioral pattern data forthe production response data; detect, based on a comparison at least oneof: the production response data to the expected response data, and theproduction behavioral pattern data to the expected behavioral patterndata, anomalous activity; and as a result of detecting the anomalousactivity, perform one or more operations to provide notice or cause oneor more of the factory's P/E/C systems to address the anomalous activityin the manufacturing process.

In some examples, the instructions further cause the system to:determine a type of anomalous activity and an associated confidencelevel; and determine the one or more operations based on the type of theanomalous activity and the associated confidence level.

In one example, a non-transitory, computer-readable storage mediumstores thereon executable instructions that, as a result of beingexecuted by one or more processors of a computer system, cause thecomputer system to: receive one or more control signals from one or moreof a factory's process, equipment and control (P/E/C) systems during amanufacturing process; generate expected response data and expectedbehavioral pattern data for the control signals; receive productionresponse data from the one or more of the factory's P/E/C systems;generate production behavioral pattern data for the production responsedata; detect, based on a comparison at least one of: the productionresponse data to the expected response data, and the productionbehavioral pattern data to the expected behavioral pattern data,anomalous activity; and as a result of detecting the anomalous activity,perform one or more operations to provide notice or cause one or more ofthe factory's P/E/C systems to address the anomalous activity in themanufacturing process.

In some examples, the executable instructions further cause the computersystem to: determine, based on the evaluation of the expected behavioralpattern data and the production behavioral pattern data, a confidencelevel associated with an identification of the anomalous activity; andidentify, based on the confidence level, the one or more operations tobe performed to provide the notice or cause one or more of the factory'sP/E/C systems to address the anomalous activity in the manufacturingprocess.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the disclosure and are nottherefore to be considered to be limiting in their scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIG. 1 illustrates an example method of providing inputs to a deeplearning processor during operation of a factory's P/E/C systems;

FIG. 2 shows an example method for training a deep learning processor;

FIG. 3 shows an example behavioral pattern for a subset of response datagenerated by a factory's P/E/C systems;

FIG. 4 shows an example method for deploying a trained deep learningprocessor to monitor and detect anomalous activity in a factory's P/E/Csystems;

FIG. 5 shows an example method for logging and creating data alerts; and

FIG. 6 shows an illustrative example of a computing system architectureincluding various components in electrical communication with each otherusing a connection in accordance with various embodiments.

DETAILED DESCRIPTION

The detailed description set forth below is intended as a description ofvarious configurations of the subject technology and is not intended torepresent the only configurations in which the subject technology can bepracticed. The appended drawings are incorporated herein and constitutea part of the detailed description. The detailed description includesspecific details for the purpose of providing a more thoroughunderstanding of the subject technology. However, it will be clear andapparent that the subject technology is not limited to the specificdetails set forth herein and may be practiced without these details. Insome instances, structures and components are shown in block diagramform in order to avoid obscuring the concepts of the subject technology.

Manufacturing at a factory relies on many process stations that areautomatically controlled. These automatically controlled processstations are vulnerable to attacks from malware, which if not detectedearly can cause interference or non-repairable damage to equipment andproduct yield. In order to understand a factory's exposure to malware,some background on the manufacturing process will be provided. Note,“manufacturing process” and “factory process” are used interchangeablyherein. While the dynamic monitoring and securing mechanisms disclosedherein refer to a manufacturing or factory's P/E/C systems, the dynamicmonitoring and securing mechanisms can also be applied to any industrialenvironment or infrastructure facility that deploys an industrialcontrol system, such as power plants, power grids, utilities,telecommunication, financial, health, and transportation facilities.

Note, the reference to P/E/C systems herein, although referred to in theplural, is understood to mean one or more of the P/E/C systems, ratherthan every one of the P/E/C systems. For example, response data from theP/E/C systems, is understood to mean response data from one or more ofthe P/E/C systems.

In particular, manufacturing is complex and comprises different processstations (or “stations”) that process raw materials until a finalproduct (referred to herein as “final output”) is produced. With theexception of the final process station, each process station receives aninput for processing and outputs an intermediate output that is passedalong to one or more subsequent (downstream) processing station foradditional processing. The final process station receives an input forprocessing and outputs the final output.

Each process station can include one or more tools/equipment thatperforms a set of process steps on: received raw materials (this canapply to a first station or any of the subsequent stations in themanufacturing process) and/or the received output from a prior station(this applies to any of the subsequent stations in the manufacturingprocess). Examples of process stations can include, but are not limitedto conveyor belts, injection molding presses, cutting machines, diestamping machines, extruders, CNC mills, grinders, assembly stations, 3Dprinters, robotic devices, quality control and validation stations.Example process steps can include: transporting outputs from onelocation to another (as performed by a conveyor belt); feeding materialinto an extruder, melting the material and injecting the materialthrough a mold cavity where it cools and hardens to the configuration ofthe cavity (as performed by an injection molding presses); cuttingmaterial into a specific shape or length (as performed by a cuttingmachine); pressing material into a particular shape (as performed by adie stamping machine).

In manufacturing processes, process stations can run in parallel or inseries. When operating in parallel, a single process station can sendits intermediate output to more than 1 stations (e.g., 1 to N stations),and a single process station can receive and combine intermediateoutputs from more than one to N stations. Moreover, a single processstation can perform the same process step or different process steps,either sequentially or non-sequentially, on the received raw material orintermediate output during a single iteration of a manufacturingprocess.

Operation of each process station can be governed by one or more processcontrollers. In some implementation, each process station has one ormore process controllers (referred to herein as “a station controller”or “a process controller”) that are programmed to control the operationof the process station (the programming algorithms referred to herein as“control algorithms”). However, in some aspects, a single processcontroller may be configured to control the operations of two or moreprocess stations. One example of a factory controller is a ProgrammableLogic Controller (PLC). A PLC can be programmed to operate manufacturingprocesses and systems. The PLC or other controller can receiveinformation from connected sensors or input devices, process the dataand generate outputs (e.g., control signals to control one or morecontrol values of an associated process station) based on pre-programmedparameters and instructions. Other examples of process controllersinclude, but are not limited to, distributed control systems (DCS) andsupervisory control and data acquisition systems (SCADA).

An operator or control algorithms can provide the station controllerwith station controller setpoints (or “setpoints” or “controllersetpoints” or CSPs) that represent a desired single value or range ofvalues for each process station control value that the stationcontroller controls. The values that can be measured during theoperation of a station's equipment or processes can either be classifiedas control values or station values. A value that is controlled by astation controller will be classified herein as control values, theother measured values will be classified herein as station values.Examples of control and/or station values include, but are not limitedto: speed, temperature, pressure, vacuum, rotation, current, voltage,power, viscosity, materials/resources used at the station, throughputrate, outage time, noxious fumes, the type of steps and order of thesteps performed at the station. Although, the examples are the same,whether a measured value is classified as a control value or a stationvalue, will depend on the particular station and whether the measuredvalue is controlled by a station controller or is simply a byproduct ofthe operation of the station. During the manufacturing process, controlvalues are provided to a station controller, while station values arenot.

The control algorithms can also include instructions for monitoringcontrol values, comparing control values to corresponding setpoints anddetermining what actions to take when the control value is not equal to(or not within a defined range of) a corresponding station controllersetpoint. For example, if the measured present value of the temperaturefor the station is below the setpoint, then a control signal may be sentby the station controller to increase the temperature of the heat sourcefor the station until the present value temperature for the stationequals the setpoint. Conventional process controllers used in amanufacturing process to control a station are limited, because theyfollow static algorithms (e.g., on/off control, PI control, PID control,Lead/Lag control) for prescribing what actions to take when a controlvalue deviates from a setpoint.

One or more sensors can be included within or coupled to each processstation. These can be physical or virtual sensors, analog or digital,that exist in a manufacturing process unrelated to the operation of deeplearning processor 118, as well as any new sensors that can be added toperform any additional measurements required by deep learning processor118. Sensors can be used to measure values generated by a manufacturingprocess such as: station values, control values, intermediate and finaloutput values. Example sensors can include, but are not limited to:rotary encoders for detecting position and speed; sensors for detectingproximity, pressure, temperature, level, flow, current and voltage;limit switches for detecting states such as presence or end-of-travellimits. Sensor, as used herein, includes both a sensing device andsignal conditioning. For example, the sensing device reacts to thestation or control values and the signal conditioner translates thatreaction to a signal that can be used and interpreted by deep learningprocessor or the station controller. Example of sensors that react totemperature are RTDs, thermocouples and platinum resistance probes.Strain gauge sensors react to pressure, vacuum, weight, change indistance among others. Proximity sensors react to objects when they arewithin a certain distance of each other or a specified tart. With all ofthese examples, the reaction must be converted to a signal that can beused by a station controller or deep learning processor. In many casesthe signal conditioning function of the sensors produce a digital signalthat is interpreted by the station controller. The signal conditionercan also produce an analog signal or TTL signal among others. Virtualsensors also known as soft sensors, smart sensors or estimators includesystem models that can receive and process data from physical sensors.

A process value, as used herein refers to a station value or controlvalue that is aggregated or averaged across an entire series of stations(or a subset of the stations) that are part of the manufacturingprocess. Process values can include, for example, total throughput time,total resources used, average temperature, average speed.

In addition to station and process values, various characteristics of aprocess station's product output (i.e., intermediate output or finaloutput) can be measured, for example: temperature, weight, productdimensions, mechanical, chemical, optical and/or electrical properties,number of design defects, the presence or absence of a defect type. Thevarious characteristics that can be measured, will be referred togenerally as “intermediate output value” or “final output value.” Theintermediate/final output value can reflect a single measuredcharacteristic of an intermediate/final output or an overall score basedon a specified set of characteristics associated with theintermediate/final output that are measured and weighted according to apredefined formula.

Mechanical properties can include hardness, compression, tack, densityand weight. Optical properties can include absorption, reflection,transmission, and refraction. Electrical properties can includeelectrical resistivity and conductivity. Chemical properties can includeenthalpy of formation, toxicity, chemical stability in a givenenvironment, flammability (the ability to burn), preferred oxidationstates, pH (acidity/alkalinity), chemical composition, boiling point,vapor point). The disclosed mechanical, optical, chemical and electricalproperties are just examples and are not intended to be limiting.

Malware can be designed to disrupt the proper functioning of a factory'sP/E/C systems in a number of ways. For instance, malware executing on acomputing device may cause a station controller to send control signalsto its associated process station(s) to operate at levels that will beharmful to the equipment itself or its output. Additionally, thismalware may cause fluctuating control values at a harmful rate or atharmful increments. Further, computing devices executing malware orother malicious applications may provide false feedback to the stationcontroller, so that the controller is not aware of harmful conditions atan associated process station and, thus, may not make neededadjustments. Malware can also be designed to target one or more sensorsto manipulate or corrupt the measured values generated by amanufacturing process. Malware can also be designed to intercept ormonitor data generated throughout the manufacturing process or datacommunicated among components involved in the manufacturing process suchas station processors, controllers, data processing servers, sensors.

While a range of IT solutions such as antivirus software, firewalls andother strategies exist to protect against the introduction of malware,malware has become more sophisticated at evading such solutions. Thedisclosed technology focuses on dynamically monitoring measured valuesand outputs from the operation and control of the factory processes,equipment and control systems, and identifying disruptions, or anyunexpected changes, whether due to the presence of malware or otherharmful or unexpected system changes. Although some conventional methodsexist (e.g., Statistical Process Control (SPC)) that provide alerts whenthe operation and control of factories exceed certain limits, they donot provide alerts when the operation and control of factories are incontrol and are limited in their ability to analyze trends across manystations or the impact of several stations together.

Accordingly, it is desirable to provide a new mechanism for securingfactory processes, equipment and control systems by dynamicallydetecting anomalous activity, however subtle, before damage to themanufacturing process occurs, and providing mechanical, digital, and/orfunctional alerts. It is also desirable to provide a mechanism thatmonitors the inputs to and outputs of each station (and their associatedcontrollers) individually, and together with the inputs to and outputsof other stations (and their associated controllers) in themanufacturing process, to dynamically identify anomalous activity and toprovide mechanical, digital and/or functional alerts. In some instances,anomalous activity can be caused by the introduction of malware, but itis understood that anomalous activity can refer more generally to othercauses, beyond malware, that interfere with a factory's P/E/C systems.

A deep learning processor based on machine-learning (ML) or artificialintelligence (AI) models may be used to evaluate control values, stationvalues, process values, data output, and/or intermediate and finaloutput values (collectively, “response data”) along with associatedstation controller setpoints, functional priors, experiential priors,and/or universal inputs to identify any variation from typical factorycontrol and operation. As understood by those of skill in the art,machine learning based techniques can vary depending on the desiredimplementation, without departing from the disclosed technology. Forexample, machine learning techniques can utilize one or more of thefollowing, alone or in combination: hidden Markov models; recurrentneural networks; convolutional neural networks (CNNs); deep-learning;Bayesian symbolic methods; reinforcement learning, general adversarialnetworks (GANs); support vector machines; image registration methods;long-term, short term memory (LSTM); and the like.

Machine learning models can also be based on clustering algorithms(e.g., a Mini-batch K-means clustering algorithm), a recommendationalgorithm (e.g., a Miniwise Hashing algorithm, or EuclideanLocality-Sensitive Hashing (LSH) algorithm), and/or an anomaly detectionalgorithm, such as a Local outlier factor. The machine learning modelscan be based on supervised and/or unsupervised methods.

Machine learning models, as discussed herein, can also be used todetermine the process stations, control, station, or process values andintermediate output values that are most influential on the final outputvalue (“key influencers”), and to optimize detecting malware attacks bytargeting the key influencers.

FIG. 1 illustrates an example deep learning processor 118 that can beconfigured to dynamically monitor for anomalous activity of any numberof (referred to herein by “N”) processing stations in a manufacturingprocess. In FIG. 1, the N processing stations of a manufacturing processare represented by process stations 122 and 142. The process stationscan operate serially or in parallel.

Setpoints, algorithms, initial input and operating instructions, systemand process updates and other control inputs to station controllers 120and 140 (steps 820 and 840 respectively), can be provided by a local orcentral data processing server 800. In some embodiments data processingserver 800 can be one or more computers on a network. In someembodiments, steps 820 and 840 can be performed manually by an operator.Data processing server 800, in some embodiments, can also receive dataoutput generated by station controllers 120 and 140 (steps 821 and 841respectively), as well as data generated by sensors coupled to or withinprocess stations 122 or 142, or from independent sensors 127 and 137.Data output, includes, but is not limited to: (i) data generated duringthe manufacturing process (e.g., data logs coupled to physical sensors,process station components, or station controller components); (ii) datareceived by or transmitted from each process station or stationcontroller and (iii) data communications and data generation patterns ofindividual or any number of process stations or station controllers(e.g., high data volumes, low data volumes, erratic data volumes,unusual data communication or data generation based on time of day,origin or destination of the data). In further embodiments, dataprocessing server 800 can receive all response data, as defined inconnection with FIGS. 2 and 4. In some embodiments, the data output canbe provided to deep learning processor 118 (step 830). In otherembodiments, in order to isolate deep learning processor 118, dataprocessing server will not provide any inputs to deep learning processor118. In some embodiments, data processing server 800 can also receivedata from related manufacturing processes occurring in remote geographiclocations and provide such data to deep learning processor 118. Infurther embodiments, data that a factory collects to perform analysis,as well as analysis data, such as in a control room, can be collected bydata processing server 800. Not all data inputs to data processingserver 800 are shown in FIG. 1.

Universal inputs 136, experiential priors 139, functional priors 138,and values from each of the N stations (e.g., 122 and 142) can beprovided to deep learning processor 118. In other embodiments, anynumber of additional deep learning processors can be used and configuredto dynamically monitor for anomalous activity of N processing stationsin a manufacturing process.

Functional priors, as used herein, refers to information relating to thefunctionality and known limitations of each process station,individually and collectively, in a manufacturing process. Thespecifications for the equipment used at the process station are allconsidered functional priors. Example functional priors can include, butare not limited to: a screw driven extruder that has a minimum andmaximum speed that the screw can rotate; a temperature control systemthat has a maximum and minimum temperature achievable based on itsheating and cooling capabilities; a pressure vessel that has a maximumpressure that it will contain before it explodes; a combustible liquidthat has a maximum temperature that can be reached before combustion.Functional priors can also include an order in which the individualstations that are part of a manufacturing process perform theirfunctions. Further, functional priors can include normal processvariations and normal process noise. Normal process variations caninclude machine tolerances (e.g., temperature control variations +/−1deg C., conveyor speed variations +/−0.1 m/min, pressure variations +/−3kPa); raw material variations, variations in cooling water temperature,variations due to operator error and normal process noise can include,for example, jitter in electrical signals and rounding errors in datacollection and recording.

Experiential priors as used herein, refers to information gained byprior experience with, for example performing the same or similarmanufacturing process; operating the same or similar stations; producingthe same or similar intermediate/final outputs; root cause analysis fordefects or failures in final outputs for the manufacturing process andsolutions. In some embodiments, experiential priors can includeacceptable final output values or unacceptable final output values.Acceptable final output values refer to an upper limit, lower limit orrange of final output values where the final output is considered “inspecification.” In other words, acceptable final output values describethe parameters for final output values that meet design specification,i.e., that are in-specification. Conversely, unacceptable final outputvalues refer to upper/lower limits or range of final output values wherethe final output is “not in specification” (i.e., describe theparameters for final output values that do not meet designspecifications). For example, based on prior experience it might beknown that an O-ring used to seal pipes, will only seal if it hascertain compression characteristics. This information can be used toestablish acceptable/unacceptable compression values for an O-ring finaloutput. In other words, all O-ring final outputs that have acceptablecompression values are able to perform their sealing functionality,while all O-ring final outputs that have unacceptable compression valuescannot perform their sealing functionality. Acceptable intermediateoutput values, which can be defined per station, refer to upper/lowerlimits or a range of intermediate output values that define theparameters for an intermediate output that can ultimately result in afinal output that is in specification, without requiring correctiveaction by other stations. Unacceptable intermediate output values, whichcan also be defined by station, refer to upper/lower limits or range ofintermediate output values that define the parameters for anintermediate output that will ultimately result in a final output thatis not in specification, unless corrective action is taken at anotherstation.

Similarly, acceptable/unacceptable parameters can be defined for othervariables relating to the manufacturing process:

Acceptable control, Upper or lower limits or range of values, station orsetpoint values defined per station for each type of control or stationvalue and setpoint, that define the parameters for, or are an indicationof, satisfactory station performance. Satisfactory performance refers to(1) the performance of the station itself (e.g., throughput rate is nottoo slow, there is no outage, noxious fumes or other harmful condition,resources are being used efficiently); and/or (2) control, station orsetpoint values that cause an in specification final output to beachievable, without requiring correction action by other stations.Unacceptable control, Upper or lower limits or range of values, stationor defined per station for each type of control, setpoint values stationor setpoint value, that define the parameters for, or are an indicationof, unsatisfactory station performance. Unsatisfactory performancerefers to (1) the performance of the station itself (e.g., throughputrate is too slow, an outage, noxious fumes or other harmful stationcondition, resources are not being used efficiently); and/or (2)control, station or setpoint values that cause an in specification finaloutput to be unachievable, unless corrective action by other stations istaken. Acceptable process Upper or lower limits or range of values forperformance each type of process value, that define the parameters for,or are an indication of, satisfactory performance of the manufacturingprocess. Satisfactory performance refers to (1) the functioning of theprocess itself (e.g., throughput rate is not too slow, there is nooutage, noxious fumes or other harmful condition, resources are beingused efficiently); and/or (2) process values that cause an inspecification final output to be achievable. Unacceptable process Upperor lower limits or range of values, performance defined for each type ofprocess value, that define the parameters for, or are an indication of,unsatisfactory process performance. Unsatisfactory performance refers to(1) the process performance itself (e.g., throughput rate is too slow,there is an outage, noxious fumes or other harmful condition, resourcesare not being used efficiently); and/or (2) process values that cause anin specification final output to be unachievable.Experiential priors can also include acceptable and unacceptablemanufacturing performance metrics. Manufacturing performance metricscalculate one or more aspects of multiple iterations of themanufacturing process (e.g., production volume for a specified timeperiod, production downtime for a specified time period, resources usedfor a specified time period or a specified number of final outputs,percentage of products not in specification for a specified time period,production volume for a particular operator, material costs associatedwith a specified number of final outputs).

Universal inputs, as used herein, refers to a value that is not specificto a particular process station, but rather to an aspect of the entiremanufacturing process, for example, a date, time of day, ambienttemperature, humidity or other environmental conditions that mightimpact the manufacturing process, operator, level of skill of theoperator, raw materials used in the process, raw material specificationssuch as color, viscosity, particle size, among other characteristicsthat are specific to the raw material, specific lot numbers and cost ofraw materials, tenure of the equipment/tools for each station,identifying information such as production work order numbers, batchnumbers, lot numbers, finished product numbers and finished productserial numbers.

Note, that the examples provided for each of functional priors,experiential priors and universal inputs represent one way to classifythese examples, other suitable classifications can be used. For example,another way to classify the input that is provided to deep learningprocessor 118 is: pre-process inputs (e.g., experiential priors,functional priors, material properties, scheduling requirements);in-process inputs (e.g., universal inputs, control values, stationvalues, intermediate values, final output values, process values);post-process inputs (e.g., manufacturing performance metrics and otheranalytics). Further, the functional and experiential priors can bedynamically updated throughout the manufacturing process.

Each process station can be controlled by one or more associated stationcontrollers (e.g., station controller 120 controls process station 122and station controller 140 controls process station 142). In anembodiment, a single station controller can control multiple processstations or control multiple control values associated with a singleprocess station. In some embodiments, deep learning processor 118 canprovide control inputs (represented by 126 and 146) based on predictiveprocess control or pre-programmed algorithms to each process stationcontroller. Predictive process control is described in U.S. patentapplication Ser. No. 16/663,245 entitled “Predictive Process Control fora Manufacturing Process,” which is hereby incorporated by referenceherein in its entirety. In other embodiments, the deep learningprocessor does not provide any inputs to the station controller.

A signal conditioner 190, 191, 192 and 193, for example a signalsplitter, amplifier, digital to analog converter, analog to digitalconverter, TTL, can be included to divide the control signals (e.g., 121is divided into 121 a and 121 b and 141 is divided into 141 a and 141 b)and the control values (e.g., 125 is divided into 125 a and 125 b and145 is divided into 145 a and 145 b) so that the control signals and thecontrol values are sent both to deep learning processor 118 and therelevant station controller (e.g., 120 or 140). The control values canbe analog or digital signals. Further, a signal conditioner, accordingto some embodiments, can be included within deep learning processor andcan convert all analog values to digital values or perform otherconditioning. Each station controller can provide one or more controlsignals (e.g., 121 and 141) that provides commands for regulating astation's control values (e.g., control values 125 and 145). Eachstation outputs an intermediate output (e.g., 124 and 144), that has anintermediate output value (134 a and 144 a respectively). Allintermediate output values and the final output value (e.g., 144, ifprocess station 142 is the final process station in the process) fromthe processing stations are provided to deep learning processor 118.Each station also outputs station values (e.g., 128 and 148) that can beprovided to deep learning processor 118. FIG. 1 also illustrates thatintermediate output 124 is sent (step 150) to one or more subsequentstations, which can represent a single station or any number of multiplestations. Station 142, as shown in FIG. 1, can receive (step 160) anintermediate input from any number of prior stations. In someembodiments, the setpoint values used by the station controllers (e.g.,controllers 120 and 140) can be sent to deep learning controller 118.Further, values relating to the manufacturing process can be measured byindependent sensors (e.g., independent sensor 127 and 137) and providedto deep learning controller 118.

It is understood that the communication among deep learning processor118, the station controllers, process stations and data processingserver 800, can use any suitable communication technologies that providethe ability to communicate with one or more other devices, and/or totransact data with a computer network. By way of example, implementedcommunication technologies can include, but are not limited to: analogtechnologies (e.g., relay logic), digital technologies (e.g., RS232,ethernet, or wireless), network technologies e.g., local area network(LAN), a wide area network (WAN), the Internet, Bluetooth technologies,Nearfield communication technologies, Secure RF technologies, and/or anyother suitable communication technologies. In some embodiments, in orderto isolate deep learning processor 118 from being infected by anymalware, deep learning processor 118 may not receive any input from anyprocess controller, data processing server 800, or from any computerconnected to a network. In some embodiments, deep learning processor 118receives input from any process controller, data processing server 800,or from any computer connected to a network, manually or indirectly, viaa memory device, that is scrubbed of any malware, before the data isprovided to the deep learning processor.

In some embodiments, operator inputs can be communicated to deeplearning processor 118, and/or any of the station controllers or processstations using any suitable input device (e.g., keyboard, mouse,joystick, touch, touch-screen, etc.).

FIG. 2 provides a method 200 for conditioning (training) a deep learningprocessor 118, according to some embodiments of the disclosed subjectmatter. The method 200 may be performed by a control system or othercomputing system that may provide hardware and/or software configured toimplement the deep learning processor 118.

In step 205, the setpoints, algorithms and other control inputs for eachstation controller in a manufacturing process can be initialized usingconventional control methods and provided to deep learning processor 118(step 215). In other embodiments, the setpoints, algorithms and othercontrol inputs for each station controller in a manufacturing processcan be provided to the station controller using predictive processcontrol (step 245), as described in U.S. patent application Ser. No.16/663,245 “Predictive Process Control for a Manufacturing Process.” Itshould be noted that control values, control algorithms, setpoints andany other information (e.g., process timing, equipment instructions,alarm alerts, emergency stops) provided to a station controller may bereferred to collectively as “station controller inputs” or “controlinputs.” In addition, as the manufacturing process iterates through theprocess stations, any control signal that is sent to the processstations, any control input that provided to the station controllers,and/or any adjusted setpoint can all be provided to deep learningprocessor 118. Further, other inputs, like functional priors 138,experiential priors 139 and universal inputs 136 can be provided to deeplearning processor 118.

In step 210, the manufacturing process iterates through all the processstations for a predetermined time period using conventional orpredictive process control methods. As discussed above, the processstations discussed herein can operate in series or in parallel. Further,a single station can perform: a single process step multiple times(sequentially or non-sequentially), or different process steps(sequentially or non-sequentially) for a single iteration of amanufacturing process. The process stations generate intermediateoutputs, or a final output if it is a final station. The intermediateoutput is transmitted to subsequent (downstream) station(s) in themanufacturing process until a final output is generated. In furtherembodiments, the manufacturing of components for a final output can beasynchronous and geographically disperse. In other words, components fora final output can be manufactured at any time or any place, notnecessarily at a time or place proximate to assembling the componentsinto a final output. For example, the headlights of a car can bemanufactured months before a car with the headlights is assembled.

As the process iterates through each station, all the values associatedwith: an individual station (e.g., control values); an output of anindividual station (e.g., station values, intermediate/final outputvalues, data output), or multiple stations (e.g., process values) aremeasured or calculated and provided to condition the machine learningalgorithms of deep learning processor 118 (steps 226, 227, 228, 229).Note, in some embodiments generated data output 226 is not provided todeep learning processor 118. In some embodiments, manufacturingperformance metrics (e.g., production volume for a specified timeperiod, production downtime for a specified time period, resources usedfor a specified time period or a specified number of final outputs,percentage of products not in specification for a specified time period,production volume for a particular operator, material costs associatedwith a specified number of final outputs) for the manufacturing processunder conventional control can be calculated and provided to deeplearning processor 118 (step 229).

Although not shown, any actions taken (or control signals generated) bythe station controller in response to a received control value from aprocess station, as well as any control input can be provided to deeplearning processor 118. Such actions can include adjusting temperature,speed, etc. Note, not all data inputs provided to deep learningprocessor 118 are shown in FIG. 2.

All inputs to deep learning processor 118 can be entered electronicallyor via manual means by an operator.

The conditioning of the machine learning models of deep learningprocessor 118 can be achieved through unsupervised learning methods.Other than functional priors 138, experiential priors 139, universalinputs 136 that are input into deep learning processor 118, deeplearning processor 118 draws inferences simply by analyzing the receiveddata that it collects during the iteration of the manufacturing process(e.g., steps 226, 227, 228 and 229). In other embodiments, deep learningprocessor 118 can be conditioned via supervised learning methods, or acombination of supervised and unsupervised methods or similar machinelearning methods. Further, the training of deep learning processor 118can be augmented by: providing deep learning processor 118 withsimulated data or data from a similar manufacturing process. In oneembodiment, deep learning processor 118 can be conditioned byimplementing deep learning processor 118 into a similar manufacturingprocess and fine-tuning the deep learning processor duringimplementation in the target manufacturing process. That is, training ofdeep learning processor 118 can be performed using a training processthat is performed before deep learning processor 118 is deployed into atarget manufacturing environment. In further embodiments, deep learningprocessor 118 can be deployed into a target manufacturing environment,trained, and when sufficiently trained, its functionality related tomonitoring the target manufacturing environment for anomalous activityand malware attacks can be enabled (as discussed in connection with FIG.4, steps 436-440).

As shown in FIG. 2, deep learning processor 118 employs machine learning(ML) models (step 239). These machine learning models can be conditionedby analyzing factory operation and control data (step 242), generatingbehavioral pattern data (step 243) for the response data generated asthe manufacturing process iterates through the process stations, and bydetermining normal process variation data and noise data (step 244).

The conditioning of the machine learning models of deep learningprocessor 118 can include receiving and analyzing factory operation andcontrol data for each setpoint used to regulate a particular controlvalue for an identified processing station. The factory operation andcontrol data can include the following: (i) the particular control valuethat corresponds to the setpoint; (ii) the other control values (andtheir corresponding setpoints) generated by the identified processstation; (iii) the station values generated by the identified processstation; (iv) the intermediate output values generated by the identifiedprocessing station; (v) the control values (and their correspondingsetpoints), station values, intermediate and final outputs generated byother process stations; (vi) universal inputs, functional priors,experiential priors; (vii) the control signals and other instructionsprovided to each process station; (viii) the control inputs provided toeach station controller; (ix) data output; (x) measured values relatingto factory control and operation received from independent sensors.Independent sensors can refer to sensors that provide measurements,beyond the sensors included in the normal manufacturing process. Sinceindependent sensors are not part of the normal manufacturing process,they are often protected from malware penetration. In some embodiments,these independent sensors are not directly tied to a single machine orprocess step and can be fluidly used to measure values from any machineor process step (e.g., a handheld device that randomly takesmeasurements during the manufacturing process). In some embodiments,independent sensors can provide its outputted values to a coupledmonitor, in addition to, or instead of, a deep learning processor 118.Values provided exclusively to a monitor, can be input manually intodeep learning processor 118, according to some embodiments. Deeplearning processor 118 can analyze factory operation and control data(step 242) to generate or learn behavioral patterns for response datagenerated at different setpoints (step 243).

Generating behavioral patterns (step 243) for the response data, for asingle station and across stations, for a single point in time or over aperiod of time, can include identifying: positive correlations; negativecorrelations; frequency; amplitude; upward or downward trends; a rate ofchange for each control value or stations value; for an identifiedresponse data, other response data that will or will not be affected ifthe identified response data changes. Response data 225 includes notonly the control value associated with a particular set point for anidentified process stations, but one or more of the following datatypes: (i) control values associated with other set points for theidentified process station; (ii) station values associated with theidentified process station; (iii) intermediate output values associatedwith the identified process station; (iv) control values associated withother process stations; (v) station values associated with other processstations; (vi) intermediate output values associated with other processstation; (vii) final output value; (viii) data output; (ix) measuredvalues relating to factory control and operation received fromindependent sensors.

Note, data is usually collected from sensors at a predefined rate. Thefrequency analysis can take into account this rate and adjust its outputvalue accordingly, so that the output value reflects the true frequencyrate, and does not reflect a rate that includes the time it takes tocollect data from the sensors. In some embodiments, the frequencyanalysis can also show rapid changes in a control value after a rise orfall and a brief stabilization period. The stabilization period can beso brief that it is barely detectable. This can be an example of anattack. Instead of a control value stabilizing at a high or at a lowpoint, a malicious signal can be provided to keep increasing ordecreasing the control value beyond an acceptable high or low. Byincreasing or decreasing shortly after stabilization, the attack canseem normal and consistent with the control value's prior increase ordecrease.

Based on analyzing: factory operation and control data (step 242),generated behavioral pattern data (step 243) and other inputs to thedeep learning processor, deep learning processor 118 can determinenormal process variations and normal process noise (step 244) to furthercondition its machine learning models. Normal process variations caninclude machine tolerances (e.g., temperature control variations +/−1deg C., conveyor speed variations +/−0.1 m/min, pressure variations +/−3kPa); raw material variations, variations in cooling water temperature,variations due to operator error and normal process noise can include,for example, jitter in electrical signals and rounding errors in datacollection and recording.

To create a robust data set for the conditioning of the machine learningmodels, setpoints (or other control inputs) corresponding to eachcontrol value of each process station can be adjusted, in a systematicmanner (e.g., from a minimum value to a maximum value), for every value(or a subset of values) that will yield in-specification final outputs.In further embodiments, setpoints (or other control inputs),corresponding to each control value of each process station can beadjusted, in a systematic manner (e.g., from a minimum value to amaximum value), for every value (or a subset of values) at which aprocess station is capable of operating (i.e., the entire range ofvalues that a process station is capable of operating at, not justlimited to what will yield in-specification final outputs). Further, anynumber and any combination of setpoints can be adjusted for trainingpurposes (step 205). The setpoints (or other control inputs) can beadjusted manually, by pre-programmed algorithms, or by predictiveprocess control.

In some embodiments, one or more setpoints (or other control inputs) canbe adjusted to values that will occur during known factory disruptions(e.g., wear and tear of a machine, insertion of a wrong component),unrelated to malware attacks, even if those values yield final outputsthat are not in-specification.

In some embodiments, deep learning processor 118 can be implementedalong with conventional standard process control systems associated withthe operation and control of a factory process. Instead of using all thedata associated with the operation and control of a factory process,deep learning processor 118 can train its machine learning algorithmsusing the same data that is provided to any standard process controlsystem used in the operation and control of a factory process.

For each setpoint adjustment or set of setpoint adjustments, themanufacturing process can iterate through the process stations for apredetermined time period (step 210) for a predetermined set of setpointadjustments, and/or when a defined event occurs (e.g., a predefinedamount of response data is collected), and provide setpoints (step 215),generate station and control values (step 228), generate intermediateand final output values (step 227), generate data output (step 226),generate process values and manufacturing performance metrics (step 229)to the deep learning processor 118. Deep learning processor 118 uses thedifferent inputs received as the manufacturing process iterates throughthe process stations, along with other inputs, to condition its machinelearning models (steps 242-244).

In some embodiments, in order to isolate deep learning processor 118from being infected by any malware, deep learning processor 118 does notreceive any input from any process controllers or from any computerconnected to a network. In some embodiments, inputs from processcontrollers or other computers connected to a network can be provided todeep learning processor 118 manually or indirectly via a memory devicethat was scrubbed of any malware after the data was uploaded to thememory device.

After, method 200 has finished iterating through the process stations(e.g., after a predetermined time period, after a predetermined set ofsetpoint adjustments, and/or when a defined event occurs (e.g., a robustdata set is generated)), then the conditioning of the machine learningmodels (steps 242-244) for deep learning processor 118 can be consideredsufficiently conditioned and ready to be deployed in a production systemto identify anomalous activity and run an alert protocol. Deep learningprocessor 118, with its conditioned machine learning models, can bedeployed to monitor a factory's P/E/C systems during operation(“production system”) for anomalous activity or malware attacks, in realtime or asynchronously. In some embodiments, before it is deployed in aproduction system, the deep learning processor with its conditionedmachine learning models, can be scrubbed of any malware. In otherembodiments, the production system has its own deep learning processorand only the conditioned machine learning models, scrubbed of anymalware, are uploaded to the deep learning processor included in theproduction system. While deployed in a production system, theconditioned machine learning models can continue to be conditioned bythe data it receives from the production system. The conditioned machinelearning models can be provided directly, or indirectly via a memorydevice, so that the memory device can be scrubbed for any malware,before the machine learning models are provided to the productionsystem's deep learning processor. In some embodiments, the conditioningof the deep learning processor, or part of its conditioning, can takeplace in the target production system. When the machine learning modelsare determined to be adequately conditioned, the functionality of thedeep learning processor that monitors a production system's data todetect anomalous activity and malware attacks (as described inconnection with FIG. 4, steps 436-440) can be enabled.

An example behavioral pattern for a subset of response data is shown,for example, in FIG. 3. The response data can empirically derived byactually adjusting setpoints associated with a process station, asdescribed in connection with FIG. 2. The x-axis represents a setpointvalue for station X, and the y-axis represents the response data value.The different lines shown in the graph 302 represent the normalbehavioral pattern of the response data for values associated withstation X, as well as the behavioral pattern of the response data forvalues associated with another station, station Y. In this example, thesetpoint that is increasing along the x-axis represents speed. Theresponse data that is shown in graph 302 include: for station X: controlvalue 325 (i.e., representing speed) that is associated with theincreasing setpoint; independent control value 323, which can represent,for example, power; station value 328, which can represent viscosity,and intermediate output value 334, which can represent diameter. Theresponse data for station Y, as shown in graph 302, include stationvalue 348, which can represent temperature, and final output value 344,which can represent weight. FIG. 3 shows the amplitude of each response.It also shows how the response data behaves when setpoint for speed isincreased: power (as represented by 323) at the station increases,diameter (as represented by 334) increases, viscosity (as represented by328) decreases. A change in the setpoint for station X also impactsstation Y, for example, temperature at station Y (as represented by 348)increases and weight (as represented by 344) increases. Behavioralpatterns can be quite complex, involving thousands of data points,across different stations, and identifying unusual behavioral patternscannot be performed by human calculation. Therefore, machine learninganalysis is needed to generate or learn behavioral patterns for theresponse data and to analyze those behavioral patterns for anomalousactivity.

FIG. 4, shows an example method 400 for deploying deep learningprocessor 118, with conditioned machine learning models (as discussed inconnection with FIG. 2, to monitor a target factory's P/E/C systemsduring the manufacturing process (in real time or asynchronously), anddetect anomalous activity and identify malware attacks.

Similar to FIG. 2, the setpoints of the process stations of amanufacturing process are initialized (step 405) and provided to deeplearning processor 118. In addition, as the manufacturing processiterates through the process stations, any control signal that is sentto the process stations, any control input that is provided to thestation controllers, any adjusted setpoint are all provided to deeplearning processor 118. In general, all factory operation and controldata can be provided to deep learning processor 118. Further, as themanufacturing process iterates through the process stations (step 410),production response data is generated (step 425) including, generatedstation and control values (step 428), generated intermediate and finaloutput values (step 427), generated data output (step 426) and generatedprocess values and manufacturing performance metrics (step 429) andprovided to deep learning processor 118 (which parallel steps 226, 227,228 and 229, described in connection with FIG. 2). Production responsedata refers to data as described in connection with steps 426-429 thatis generated from the P/E/C systems during the manufacturing process(the “production process”).

Deep learning processor 118 employs its conditioned machine learningalgorithms (step 435) to analyze control signals, control inputs andprior factory operation and control data to predict and generateexpected response data and expected behavioral pattern data (step 436).Based on its understanding of factory operation and control data andsuch data's correlation to specific control signals, the deep learningprocessor can predict, for the received control signals from theproduction system, and generate expected response data and correspondingbehavioral patterns.

At step 437, deep learning processor analyzes the production responsedata (i.e., response data generated during the production process) andgenerates behavioral pattern data for the production response data(“production behavioral pattern data”).

At step 438, deep learning processor compares the expected response datato the production response data and/or compares the expected behavioralpattern data to the production behavioral pattern data to identifyanomalous activity and malware attacks and generate a confidence levelfor the anomalous activity and/or malware attacks (step 438). In someaspects, the confidence level may be expressed as a numericalprobability of accuracy for the prediction, in other aspects, theconfidence level may be expressed as an interval or probability range.

By conditioning the machine learning models incorporated into the deeplearning processor, as discussed in connection with FIG. 2, including:(1) capturing extensive and diverse data across a factory's P/E/Csystems and (2) analyzing changes and normal process variations andnoise data in a factory's P/E/C systems during a factory's operation,and how the components in the P/E/C systems respond to those changes andvariations, the deep learning processor target production system (asshown in FIG. 4) can learn to recognize any deviations in the productionresponse data and production behavioral pattern data, even minisculedeviations, from expected response data and behavioral patterns in asingle component or across many components in the target factory's P/E/Csystems to identify anomalous activity and/or malware attacks (step439).

An operator or algorithm can assign thresholds to the confidence levelsassociated with anomalous activities in a factory's P/E/C systems. Oneor more predefined actions (referred to herein as “alert protocols”) canbe initiated when a threshold confidence level is met to address theanomalous activity (step 440). For example, for anomalous activitiesreceiving a high confidence level score, an alert protocol can beinitiated by deep learning processor 118 and communicated to a computersystem in a factory's P/E/C systems to run the initiated alert protocol,whereas with anomalous activities receiving lower confidence levelscores, an operator can be prompted to review the anomalous activitybefore an alert protocol is initiated. In one embodiment, the confidencelevels can be divided into three intervals: high, medium and low, and athreshold can be assigned to each interval. Further, actions to beperformed can be assigned to each interval. For example, for confidencelevels that fall into the high confidence interval an alert protocol canbe initiated, for confidence levels that fall into the medium confidenceinterval, an operator can be prompted to review the anomalous activity,for confidence levels that fall into the low confidence level interval,the anomalous activity can be flagged and sporadically checked. Thethresholds and interval ranges can be reviewed and adjusted to minimizefalse positives or false negatives. In other embodiments, the confidencelevels can be divided into two or more intervals.

In some embodiments, different alert protocols can be assigned to thedifferent intervals. For example, if an anomalous activity has aconfidence level that corresponds to a high interval, the alert protocolcan trigger a strong action, like shutting down the entire factoryprocess. Whereas if the anomalous activity has a confidence level thatcorresponds to a lower interval, an alert protocol can trigger a moremoderate action like generating a report, email or other notifications.

In further embodiments, the conditioned machine learning models of thedeep learning processor, based on its conditioning, can determine thetype of anomalous activity and different alert protocols can be assignedto the different types of anomalies detected that meet a predefinedconfidence level threshold. The alert protocol initiated can be a set ofactions designed to compensate or correct for the type of anomalousactivity detected.

The alert protocol can be mechanical (e.g., signaling an alert by siren,flashing light or other indicator), digital (e.g., printing a report,emailing a supervisor, notifying a control panel), functional (e.g.,stopping any or all of a factory's P/E/C systems, adjusting the settingsof any or all of a factory's P/E/C systems), or any combination of theabove. The alert protocol can be initiated by deep learning processor118 and communicated to another computer in the factory's P/E/C systemto run the alert protocol. Functional protocols can be implemented bycommunication with a factory's process controllers (e.g., sendingsignals 126 and 146). The protocol can be a digital activation ofindividual relays, controlled by TTL logic, ladder logic or otherprogrammable commands communicated to external devices such as stationcontrollers, PLCs or other. The protocol and command structure areincorporated into deep learning processor 118. Deep learning processor118 can include programming to allow any of these. Input to the deeplearning processor 118 can, in some embodiments, be performed, viamanual input by keyboard entry. This helps maintain the integrity ofdeep learning processor 118. In further embodiments digital entry suchas with a thumb drive or network connection can also be allowed.

In some embodiments, in real-time, during operation of a factory's P/E/Csystems, or asynchronously, the conditioned machine learning algorithmscan detect among the thousands of data points generated during themanufacturing process, at a single station or across stations, for asingle point in time or over a period of time, whether there are anyunusual: correlation patterns; frequency patterns; amplitude patterns;upward or downward trends; rate of change for a control value or stationvalue. In some embodiments, the behavioral pattern of product responsedata can be compared to the expected behavioral pattern for expectedresponse data with respect to the nominal setpoint values and thebehavioral data in the frequency domain. The deep learning controllercan analyze not just the static statistical representation but focus onthe response of the system to a planned or unplanned change in the setpoint value and directly compare that to expected performance, ascompounded during the training phase through the entire operational lifecycle of the system.

Further, deep learning processor 118 can identify whether or not theanomalous activity is a malware attack, and a confidence level for itsidentification and run an alert protocol. For example, when productionbehavioral pattern data indicates significant, sudden, rapid orunexpected changes in the production response data that is differentfrom the expected behavioral data or response data. In one embodiment,deep learning processor 118 can analyze whether the productionbehavioral pattern data is consistent with behavioral pattern data forknown disruptive activity that is not a malware attack. In someembodiments deep learning processor 118 uses data output generatedduring the manufacturing process and/or data from data logging module510 to determine whether the anomalous activity was caused by an attackor by some other failure (e.g., the material used was defective, afaulty component was installed).

An operator or algorithm can assign thresholds to the confidence levelsassociated with activities of a factory's P/E/C systems identified to bea malware attack and can predefine actions (referred to herein as “alertprotocols”) to be initiated when a threshold is met. For example, fordetected malware attacks receiving a high confidence level score, analert protocol can be initiated by deep learning processor 118 andcommunicated to another computer in the factory's P/E/C systems to runthe alert protocol, whereas with detected malware attacks receivinglower confidence level scores, an operator can be prompted to review thedetected malware attack before an alert protocol is initiated. In oneembodiment, the confidence levels can be divided into three intervals:high, medium and low, and a threshold can be assigned to each interval.In other embodiments, confidence levels can be divided into two or moreintervals. Further, actions to be performed can be assigned to eachinterval. For example, for confidence levels that fall into the highconfidence interval an alert protocol can be initiated, for confidencelevels that fall into the medium confidence interval, an operator can beprompted to review the detected malware attack, for confidence levelsthat fall into the low confidence level interval, the detected malwareattack can be flagged and sporadically checked. The thresholds andinterval ranges can be reviewed and adjusted to minimize false positivesor false negatives.

In some embodiments, different alert protocols can be assigned to thedifferent intervals. For example, if the detected malware attack has aconfidence level that corresponds to a high interval, the alert protocolcan trigger a strong action, like shutting down the entire factoryprocess. Whereas if the detected malware attack has a confidence levelthat corresponds to a lower interval, an alert protocol can trigger amore moderate action like generating a report, email or othernotifications that can identify the malware attack and suggest immediatecorrective actions to counter the attack. In further embodiments,different alert protocols can be assigned to different types of types ofmalware attacks detected that meet a predefined confidence levelthreshold, and the proper alert protocol is initiated by deep learningprocessor 118 for the attack that is detected, when it exceeds apredefined confidence level. The alert protocol can be a set of actionsdesigned to compensate or correct for the type of malware attackdetected. For example, if a corrupted sensor is detected, deep learningprocessor 118 can initiate an alert protocol to change specific valuesand settings to mitigate the alteration of the corrupted sensor. Thisallows factory production to proceed without downtime.

In some embodiments, when the confidence level exceeds a predefinedlimit indicating a malware attack, deep learning processor 118 canautomatically run a generative adversarial network or a secondartificial intelligence model, collectively called a confirmation test,to confirm or deny the attack. If the confirmation test is confirmed,the malware attack level can be raised to the highest alert. If theconfirmation test is denied the confidence level of the original modelcan be assumed and reverts to the second highest alert level. Aspreviously indicated separate alert protocols may be specified for eachalert level.

In some embodiments, deep learning processor 118 can be configured tocommunicate with existing IT security systems to notify the systems ofthe anomalous activity. In further embodiments, deep learning processor118 can be configured to communicate with a data logging module, asshown in FIG. 6. This communication can provide alerts specifying theexact source of the malware attack and also be used to reconfigurefirewall and other IT infrastructure to better defend the factoryprocesses and equipment.

In some embodiments, deep learning processor 118 can be configured tocommunicate with the supply chain management system to alert aprocurement or manufacturing source of an infected process component.

In some embodiments, deep learning processor 118 can be configured tocommunicate with the station or component that is the source for theanomalous activity and instruct the station or component to generate analert via a coupled display or media system (e.g., a sound alert) thatidentifies the existence of anomalous activity, the source for theanomalous activity and/or the type of anomalous activity.

In some embodiments, deep learning processor 118 can be implementedalong with conventional standard process control systems. Instead ofanalyzing all the data associated with the operation and control of afactory process for anomalous activity, deep learning processor canreceive the same data that is provided to any standard process controlsystems used in the operation and control of a factory process, and onlyanalyze that data for anomalous activity.

FIG. 5 shows an example data logging and output module 510 that can beconfigured to receive data from deep learning processor 118, and dataprocessing server 800 to analyze the data and to generate reports,emails, alerts, log files or other data compilations (step 515). Forexample, data logging module 510 can be programmed to search thereceived data for predefined triggering events, and to generate reports,emails, alerts, log files, updates to a monitoring dashboard, or otherdata compilations showing relevant data associated with those triggeringevents (step 515). For example, identification of anomalous activity canbe defined as a triggering event and the following data can be reported:behavioral pattern for the response data compared to the expectedbehavioral pattern, the station(s), controller(s) or sensor(s) that wereimpacted by the anomalous activity, the sensor(s) that generated thetriggering event, identification of the specific response data that isunexpected, date and time of day that the anomalous activity occurred,the confidence level associated with the triggering event, the impact ofthe anomalous activity on other stations and the intermediate or finaloutput. Other suitable triggers can be defined, and other suitable datacan be reported. In some embodiments, data logging module 510 can beincluded within deep learning processor 118. In some embodiments, datafrom the data logging module can be provided to deep learning processor118 as part of the response data, as discussed in connection with FIGS.2 and 4 or to initiate an alert protocol.

In some embodiments, it is useful to identify what parameters of themanufacturing process most impact the final output value or the processperformance (the “key influencers”). The deep learning processor 118 canconsider all parameters of the manufacturing process (e.g., one or morecontrol values, one or more station values, one or more process values,one or more stations, one or more intermediate outputs, experientialpriors (e.g., root cause analysis for defects or failures in finaloutputs for the manufacturing process and solutions), functional priors,universal inputs or any combination thereof), and using one or more ofits machine learning algorithms can identify the key influencers. Insome aspects, deep learning processor 118 can employ unsupervisedmachine learning techniques to discover one or more key influencers, forexample, wherein each key influencer is associated with one or moreparameters (or parameter combinations) that affect characteristics ofvarious station outputs, the final output, and/or process performance.It is understood that discovery of key influencers and their associatedparameters may be performed through operation and training of deeplearning processor 118, without the need to explicitly label, identifyor otherwise output key influencers or parameters to a human operator.

In some approaches, deep learning processor 118, using its machinelearning models, can rank or otherwise generate an ordering of, in orderof significance, the impact of each parameter of the manufacturingprocess on the final output value or the process performance. A keyinfluencer can be identified based on: a cutoff ranking (e.g., the top 5aspects of the manufacturing process that impact the final outputvalue), a minimum level of influence (e.g., all aspects of themanufacturing process that contribute at least 25% to the final outputvalue); critical process stations or operations that malware is likelyto target; or any other suitable criteria. In some aspects, keyinfluence characteristics may be associated with a quantitative score,for example, that is relative to the weight of influence for thecorresponding characteristic.

Deep learning processor 118 can continuously, throughout themanufacturing process, calculate and refine the key influencers. The keyinfluencers can be used to help build a more robust data set to traindeep learning processor 118. Instead of varying every single controlinput in the manufacturing process to generate a robust data set, or anarbitrary subset of control inputs, deep learning process 118 can varyonly the control inputs (e.g., setpoints) associated with the keyinfluencers to generate a robust data set. In further embodiments, deeplearning processor 118 can use the key influencers to identify whichstations and response data to monitor, to detect anomalous activity.Identifying key influencers is further described in U.S. patentapplication Ser. No. 16/663,245 “Predictive Process Control for aManufacturing Process.”

FIG. 6 shows the general configuration of an embodiment of deep learningprocessor 118 that can implement dynamic monitoring and securing offactory processes, equipment and automated systems , in accordance withsome embodiments of the disclosed subject matter. Although deep learningprocessor 118 is illustrated as a localized computing system in whichvarious components are coupled via a bus 605, it is understood thatvarious components and functional computational units (modules) can beimplemented as separate physical or virtual systems. For example, one ormore components and/or modules can be implemented in physically separateand remote devices, such as, using virtual processes (e.g., virtualmachines or containers) instantiated in a cloud environment.

Deep learning processor 118 can include a processing unit (e.g., CPU/sand/or processor/s) 610 and bus 605 that couples various systemcomponents including system memory 615, such as read only memory (ROM)620 and random access memory (RAM) 625, to processing unit 610.Processing unit 610 can include one or more processors such as aprocessor from the Motorola family of microprocessors or the MIPS familyof microprocessors. In an alternative embodiment, the processing unit610 can be specially designed hardware for controlling the operations ofdeep learning processor 118 and performing predictive process control.When acting under the control of appropriate software or firmware,processing module 610 can perform various machine learning algorithmsand computations described herein.

Memory 615 can include various memory types with different performance.characteristics, such as memory cache 612. Processor 610 can be coupledto storage device 630, which can be configured to store software andinstructions necessary for implementing one or more functional modulesand/or database systems. Each of these modules and/or database systemscan be configured to control processor 610 as well as a special-purposeprocessor where software instructions are incorporated into the actualprocessor design.

To enable operator interaction with deep learning processor 118, inputdevice 645 can represent any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input and so forth. An output device 635can also be one or more of a number of output mechanisms (e.g., printer,monitor) known to those of skill in the art. In some instances,multimodal systems can enable an operator to provide multiple types ofinput to communicate with deep learning processor 118. Communicationsinterface 640 can generally govern and manage the operator input andsystem output, as well as all electronic input received from and sent toother components that are part of a manufacturing process such as thestation controllers, process stations, data logging module, and allassociated sensors and image capturing devices. There is no restrictionon operating on any particular hardware arrangement and therefore thebasic features here may easily be substituted for improved hardware orfirmware arrangements as they are developed. Data output from deepcontroller 118 can be displayed visually, printed, or generated in fileform and stored in storage device 630 or transmitted to other componentsfor further processing.

Communication interface 640 can be provided as interface cards(sometimes referred to as “line cards”). Generally, they control thesending and receiving of data packets over the network and sometimessupport other peripherals used with the router. Among the interfacesthat can be provided are Ethernet interfaces, frame relay interfaces,cable interfaces, DSL interfaces, token ring interfaces, and the like.In addition, various very high-speed interfaces may be provided such asfast token ring interfaces, wireless interfaces, Ethernet interfaces,Gigabit Ethernet interfaces, ATM interfaces, HSSI interfaces, POSinterfaces, FDDI interfaces and the like. Generally, these interfacesmay include ports appropriate for communication with the appropriatemedia. In some cases, they may also include an independent processorand, in some instances, volatile RAM. The independent processors maycontrol such communications intensive tasks as packet switching, mediacontrol and management. By providing separate processors for thecommunications intensive tasks, these interfaces allow processing unit610 to efficiently perform machine learning and other computationsnecessary to implement predictive process control. Communicationinterface 640 can be configured to communicate with the other componentsthat are part of a manufacturing process such as the stationcontrollers, process stations, data logging module, and all associatedsensors and image capturing devices.

In some embodiments, deep learning processor 118 can include an imagingprocessing device 670 that processes images received by various imagecapturing devices such as video cameras, that are coupled one or moreprocessing station and are capable of monitoring and capturing images ofintermediate and final outputs. These images can be transmitted to deeplearning processor 118 via communication interface 640, and processed byimage processing device 670. The images can be processed to providedata, such as number and type of defects, output dimensions, throughput,that can be used by deep learning processor 118 to compute intermediateand final output values. In some embodiments, the image processingdevice can be external to deep learning processor 118 and provideinformation to deep learning processor 118 via communication interface640.

Storage device 630 is a non-transitory memory and can be a hard disk orother types of computer readable media that can store data accessible bya computer, such as magnetic cassettes, flash memory cards, solid statememory devices, digital versatile disks, cartridges, random accessmemories (RAMs) 625, read only memory (ROM) 620, and hybrids thereof.

In practice, storage device 630 can be configured to receive, store andupdate input data to and output data from deep learning processor 118,for example functional priors, experiential priors, universal input;pre-process inputs; in-process inputs and post-process inputs.

In some embodiments, any suitable computer readable media can be usedfor storing instructions for performing the functions and/or processesdescribed herein. For example, in some embodiments, computer readablemedia can be transitory or non-transitory. For example, non-transitorycomputer readable media can include media such as non-transitorymagnetic media (such as hard disks, floppy disks, etc.), non-transitoryoptical media (such as compact discs, digital video discs, Blu-raydiscs, etc.), non-transitory semiconductor media (such as flash memory,electrically programmable read only memory (EPROM), electricallyerasable programmable read only memory (EEPROM), etc.), any suitablemedia that is not fleeting or devoid of any semblance of permanenceduring transmission, and/or any suitable tangible media. As anotherexample, transitory computer readable media can include signals onnetworks, in wires, conductors, optical fibers, circuits, and anysuitable media that is fleeting and devoid of any semblance ofpermanence during transmission, and/or any suitable intangible media.

The various systems, methods, and computer readable media describedherein can be implemented as part of a cloud network environment. Asused in this paper, a cloud-based computing system is a system thatprovides virtualized computing resources, software and/or information toclient devices. The computing resources, software and/or information canbe virtualized by maintaining centralized services and resources thatthe edge devices can access over a communication interface, such as anetwork. The cloud can provide various cloud computing services viacloud elements, such as software as a service (SaaS) (e.g.,collaboration services, email services, enterprise resource planningservices, content services, communication services, etc.),infrastructure as a service (IaaS) (e.g., security services, networkingservices, systems management services, etc.), platform as a service(PaaS) (e.g., web services, streaming services, application developmentservices, etc.), and other types of services such as desktop as aservice (DaaS), information technology management as a service (ITaaS),managed software as a service (MSaaS), mobile backend as a service(MBaaS), etc.

The provision of the examples described herein (as well as clausesphrased as “such as,” “e.g.,” “including,” and the like) should not beinterpreted as limiting the claimed subject matter to the specificexamples; rather, the examples are intended to illustrate only some ofmany possible aspects. A person of ordinary skill in the art wouldunderstand that the term mechanism can encompass hardware, software,firmware, or any suitable combination thereof.

Unless specifically stated otherwise as apparent from the abovediscussion, it is appreciated that throughout the description,discussions utilizing terms such as “determining,” “providing,”“identifying,” “comparing” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system memories or registersor other such information storage, transmission or display devices.Certain aspects of the present disclosure include process steps andinstructions described herein in the form of an algorithm. It should benoted that the process steps and instructions of the present disclosurecould be embodied in software, firmware or hardware, and when embodiedin software, could be downloaded to reside on and be operated fromdifferent platforms used by real time network operating systems.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored on acomputer readable medium that can be accessed by the computer. Such acomputer program may be stored in a computer readable storage medium,such as, but is not limited to, any type of disk including floppy disks,optical disks, CD-ROMs, magnetic-optical disks, read-only memories(ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic oroptical cards, application specific integrated circuits (ASICs), or anytype of non-transient computer-readable storage medium suitable forstoring electronic instructions. Furthermore, the computers referred toin the specification may include a single processor or may bearchitectures employing multiple processor designs for increasedcomputing capability.

The algorithms and operations presented herein are not inherentlyrelated to any particular computer or other apparatus. Variousgeneral-purpose systems may also be used with programs in accordancewith the teachings herein, or it may prove convenient to construct morespecialized apparatus to perform the required method steps andsystem-related actions. The required structure for a variety of thesesystems will be apparent to those of skill in the art, along withequivalent variations. In addition, the present disclosure is notdescribed with reference to any particular programming language. It isappreciated that a variety of programming languages may be used toimplement the teachings of the present disclosure as described herein,and any references to specific languages are provided for disclosure ofenablement and best mode of the present disclosure.

The logical operations of the various embodiments are implemented as:(1) a sequence of computer implemented steps, operations, or proceduresrunning on a programmable circuit within a general use computer, (2) asequence of computer implemented steps, operations, or proceduresrunning on a specific-use programmable circuit; and/or (3)interconnected machine modules or program engines within theprogrammable circuits. The system can practice all or part of therecited methods, can be a part of the recited systems, and/or canoperate according to instructions in the recited non-transitorycomputer-readable storage media. Such logical operations can beimplemented as modules configured to control the processor to performparticular functions according to the programming of the module.

It is understood that any specific order or hierarchy of steps in theprocesses disclosed is an illustration of exemplary approaches. Basedupon design preferences, it is understood that the specific order orhierarchy of steps in the processes may be rearranged, or that only aportion of the illustrated steps be performed. Some of the steps may beperformed simultaneously. For example, in certain circumstances,multitasking and parallel processing may be advantageous. Moreover, theseparation of various system components in the embodiments describedabove should not be understood as requiring such separation in allembodiments, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products. Theapparatus, method and system for dynamic monitoring and securing offactory processes, equipment and automated systems have been describedin detail with specific reference to these illustrated embodiments. Itwill be apparent, however, that various modifications and changes can bemade within the spirit and scope of the disclosure as described in theforegoing specification, and such modifications and changes are to beconsidered equivalents and part of this disclosure.

STATEMENTS OF THE DISCLOSURE

Statement 1: a computer-implemented method, comprising: receiving, by adeep learning processor, one or more control signals from one or more ofa factory's process, equipment and control (P/E/C) systems during amanufacturing process; generating, by a deep learning processor,expected response data and expected behavioral pattern data for thecontrol signals; receiving, by the deep learning processor, productionresponse data, from the one or more of the factory's P/E/C systems;generating, by the deep learning processor, production behavioralpattern data for the production response data; comparing at least oneof: (i) the production response data to the expected response data, and(ii) the production behavioral pattern data to the expected behavioralpattern data to detect anomalous activity; and as a result of detectingthe anomalous activity, performing one or more operations to providenotice or cause one or more of the factory's P/E/C systems to addressthe anomalous activity in the manufacturing process.

Statement 2: the computer-implemented method of statement 1, wherein theone or more operations include: determining whether the anomalousactivity is a malware attack; and as a result of a determination thatthe anomalous activity is the malware attack, initiating an alertprotocol to provide notice or cause one or more of the factory's P/E/Csystems to address the anomalous activity in the manufacturing process.

Statement 3: the computer-implemented method of any of statements 1 to2, wherein the one or more operations include shutting down themanufacturing process.

Statement 4: the computer-implemented method of any of statements 1 to3, wherein the production response data is derived by adjustingsetpoints associated with one or more process stations associated withthe one or more of the factory's P/E/C systems.

Statement 5: the computer-implemented method of any of statements 1 to4, wherein the anomalous activity is detected as a result of theproduction response data and the expected response data indicating adeviation.

Statement 6: the computer-implemented method of any of statements 1 to5, wherein the anomalous activity is detected as a result of theproduction behavioral pattern data and the expected behavioral patterndata indicating a deviation.

Statement 7: the computer-implemented method of any of statements 1 to6, wherein the one or more operations include transmitting anotification to an operator of the manufacturing process to review theanomalous activity.

Statement 8: the computer-implemented method of any of statements 1 to7, further comprising: determining, based on a comparison of theproduction response data to the expected response data, a confidencelevel associated with an identification of the anomalous activity; andidentifying, based on the confidence level, the one or more operationsto be performed to provide notice or cause one or ore of the factory'sP/E/C systems to address the anomalous activity in the manufacturingprocess.

Statement 9: the computer-implemented method of any of statements 1 to8, further comprising: determining, based on the comparison ofproduction behavioral pattern data to the expected behavioral patterndata, a confidence level associated with an identification of theanomalous activity; and identifying, based on the confidence level, theone or more operations to be performed to provide notice or cause one ormore of the factory's P/E/C systems to address the anomalous activity inthe manufacturing process.

Statement 10: a system, comprising: one or more processors; and memorystoring thereon instructions that, as a result of being executed by theone or more processors, cause the system to: receive one or more controlsignals from one or more of a factory's process, equipment and control(P/E/C) systems during a manufacturing process; generate expectedresponse data and expected behavioral pattern data for the controlsignals; receive production response data from the one or more of thefactory's P/E/C systems; generate production behavioral pattern data forthe production response data; detect, based on a comparison at least oneof: (i) the production response data to the expected response data, and(ii) the production behavioral pattern data to the expected behavioralpattern data, anomalous activity; and as a result of detecting theanomalous activity, perform one or more operations to provide notice orcause one or more of the factory's P/E/C systems to address theanomalous activity in the manufacturing process.

Statement 11: the system of statement 10, wherein the instructionsfurther cause the system to: determine a type of anomalous activity andan associated confidence level; and determine the one or more operationsbased on the type of the anomalous activity and the associatedconfidence level.

Statement 12: the system of any of statements 10 to 11, wherein the oneor more operations include shutting down the manufacturing process.

Statement 13: the system of any of statements 10 to 12, wherein the oneor more operations include transmitting a notification to an operator ofthe manufacturing process to review the anomalous activity.

Statement 14: the system of any of statements 10 to 13, wherein the oneor more operations include: determining whether the anomalous activityis a malware attack; and as a result of a determination that theanomalous activity is the malware attack, initiating an alert protocolto provide the notice or cause one or more of the factory's P/E/Csystems to address the anomalous activity in the manufacturing process.

Statement 15: the system of statement 14, wherein the alert protocol isa digital activation of individual relays communicated to one or moredevices associated with the factory's P/E/C systems to provide thenotice or cause one or more of the factory's P/E/C systems to addressthe anomalous activity in the manufacturing process.

Statement 16: a non-transitory, computer-readable storage medium storingthereon executable instructions that, as a result of being executed byone or more processors of a computer system, cause the computer systemto: receive one or more control signals from one or more of a factory'sprocess, equipment and control (P/E/C) systems during a manufacturingprocess; generate expected response data and expected behavioral patterndata for the control signals; receive production response data from theone or more of the factory's P/E/C systems; generate productionbehavioral pattern data for the production response data; detect, basedon a comparison at least one of: (i) the production response data to theexpected response data, and (ii) the production behavioral pattern datato the expected behavioral pattern data, anomalous activity; and as aresult of detecting the anomalous activity, perform one or moreoperations to provide notice or cause one or more of the factory's P/E/Csystems to address the anomalous activity in the manufacturing process.

Statement 17: the non-transitory, computer-readable storage medium ofstatement 16, wherein the one or more operations include transmitting anotification to an operator of the manufacturing process to review theanomalous activity.

Statement 18: the non-transitory, computer-readable storage medium ofany of statements 16 to 17, wherein the one or more operations include:determining whether the anomalous activity is a malware attack; and as aresult of a determination that the anomalous activity is the malwareattack, initiating an alert protocol to provide the notice or cause oneor more of the factory's P/E/C systems to address the anomalous activityin the manufacturing process.

Statement 19: the non-transitory, computer-readable storage medium ofany of statements 16 to 18, wherein the one or more operations includeshutting down the manufacturing process.

Statement 20: the non-transitory, computer-readable storage medium ofany of statements 16 to 19, wherein the executable instructions furthercause the computer system to: determine, based on the evaluation of theexpected behavioral pattern data and the production behavioral patterndata, a confidence level associated with an identification of theanomalous activity; and identify, based on the confidence level, the oneor more operations to be performed to provide the notice or cause one ormore of the factory's P/E/C systems to address the anomalous activity inthe manufacturing process.

What is claimed:
 1. A manufacturing system, comprising: two or moreprocess stations, wherein a first process station is logicallypositioned upstream of a second process station, wherein each of thefirst process station and the second process station is configured toperform a step of a manufacturing process; a first station controllerprogrammed to control an operation of the first process station; asecond station controller programmed to control an operation of thesecond process station; a deep learning controller in communication withthe two or more process stations, the first station controller, and thesecond station controller, wherein the deep learning controller istrained to identify anomalous activity in the manufacturing process; afirst signal splitter positioned between the first station controllerand the first process station, the first signal splitter configured tosplit a control signal transmitted from the first station controller tothe first process station, wherein a first portion of the control signalis provided to the deep learning controller and a second portion of thecontrol signal is provided to the first process station; and a secondsignal splitter positioned downstream of the first process station, thesecond signal splitter configured to split control values output by thefirst process station, wherein a first portion of the control values isprovided to the deep learning controller and a second portion of thecontrol values is provided to the first station controller.
 2. Thesystem of claim 1, wherein the deep learning controller is furtherconfigured to generate expected response data and expected behavioralpattern data based on the first portion of the control signal and thefirst portion of the control values.
 3. The system of claim 2, whereinthe deep learning controller is further configured to compare theexpected response data to actual response data generated during a firststep of the manufacturing process.
 4. The system of claim 2, wherein thedeep learning controller is further configured to compare the expectedbehavioral pattern data to actual behavioral pattern data during a firststep of the manufacturing process.
 5. The system of claim 1, wherein thedeep learning controller is further configured to identify that theanomalous activity is a malware attack.
 6. The system of claim 5,wherein the deep learning controller is further configured to execute analert protocol when the anomalous activity is a malware attack.
 7. Thesystem of claim 6, wherein the alert protocol is digitally shutting downthe manufacturing process.
 8. The system of claim 6, wherein the alertprotocol is an electronic notification.
 9. The system of claim 6,wherein the alert protocol is digitally adjusting setpoints associatedwith downstream process stations.
 10. A manufacturing system,comprising: a process station configured to perform a step of amanufacturing process; a station controller programmed to control anoperation of the process station; a deep learning controller incommunication with the process station and the station controller,wherein the deep learning controller is trained to identify anomalousactivity in the manufacturing process based on response data receivedfrom the station controller; a first signal splitter positioned betweenthe station controller and the process station, the first signalsplitter configured to split a control signal transmitted from thestation controller to the process station, wherein a first portion ofthe control signal is provided to the deep learning controller and asecond portion of the control signal is provided to the process station;and a second signal splitter positioned downstream of the processstation, the second signal splitter configured to split control valuesoutput by the process station, wherein a first portion of the controlvalues is provided to the deep learning controller and a second portionof the control values is provided to the station controller.
 11. Thesystem of claim 10, wherein the deep learning controller is furtherconfigured to generate expected response data and expected behavioralpattern data based on the first portion of the control signal and thefirst portion of the control values.
 12. The system of claim 11, whereinthe deep learning controller is further configured to compare theexpected response data to actual response data generated during a firststep of the manufacturing process.
 13. The system of claim 11, whereinthe deep learning controller is further configured to compare theexpected behavioral pattern data to actual behavioral pattern dataduring a first step of the manufacturing process.
 14. The system ofclaim 10, wherein the deep learning controller is further configured toidentify that the anomalous activity is a malware attack.
 15. The systemof claim 14, wherein the deep learning controller is further configuredto execute an alert protocol when the anomalous activity is a malwareattack.
 16. The system of claim 15, wherein the alert protocol isdigitally shutting down the manufacturing process.
 17. The system ofclaim 15, wherein the alert protocol is an electronic notification. 18.The system of claim 15, wherein the alert protocol is digitallyadjusting setpoints associated with downstream process stations.
 19. Acomputer-implemented method in a manufacturing process comprising:receiving, by a deep learning controller, a first portion of a controlsignal from a station controller, wherein the control signal is splitbetween the first portion and a second portion sent to a process stationassociated with the station controller; receiving, by the deep learningcontroller, a first portion of control values from the process station,wherein the control values are split between the first portion of thecontrol values and a second portion of the control values provided tothe station controller; generate, by the deep learning controller,expected response data and expected behavioral pattern data based on thefirst portion of the control signal and the first portion of the controlvalues; and identify, by the deep learning controller, whether there isanomalous activity in the manufacturing process based on at least one ofthe expected response data and the expected behavioral pattern data. 20.The computer-implemented method of claim 19, further comprising:determining, by the deep learning controller, that there is anomalousactivity in the manufacturing process; and executing, by the deeplearning controller, an alert protocol based on the determining.